for (i in 1:length(params))
print(paste('Parameter:', names(params)[i], ' - Value:', params[[i]], '- Class:', class(params[[i]])))
## [1] "Parameter: Dataset - Value: CHD2_iPSCs_and_organoids_PublicRepo - Class: character"
## [1] "Parameter: SEFile - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.SE_deseq2_AR.rds - Class: character"
## [1] "Parameter: DEAList - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.DEAList_AR.rds - Class: character"
## [1] "Parameter: AR - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/2.DEA/iPSCs/Output/Savings/ipsc.deseqARvsWT.rds - Class: character"
## [1] "Parameter: SavingFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/iPSCs/Output/Savings/ - Class: character"
## [1] "Parameter: FiguresFolder - Value: /group/testa/Project/CHD2/BulkRNAseq/results/PublicRepo/6.Enrichments/iPSCs/Output/Figures/ - Class: character"
## [1] "Parameter: FDRthr - Value: 0.05 - Class: numeric"
## [1] "Parameter: logFCthr - Value: 0.55 - Class: numeric"
## [1] "Parameter: TopGO - Value: BP_MF_CC - Class: character"
## [1] "Parameter: GoEnTh - Value: 1 - Class: numeric"
## [1] "Parameter: GoPvalTh - Value: 0.05 - Class: numeric"
## [1] "Parameter: NbName - Value: TopGO_iPSCs_AR - Class: character"
## [1] "Parameter: SaveImages - Value: FALSE - Class: logical"library(RNASeqBulkExploratory)
library(SummarizedExperiment)
library(tidyr)
library(AnnotationDbi)
library(org.Hs.eg.db)
library(topGO)
library(sechm)
library(ggplot2)
library(grid)
library(gridExtra)
library(RColorBrewer)
library(cowplot)
source('/group/testa/Users/oliviero.leonardi/myProjects/CHD2/BulkRNAseq/ContainerHome/CHD2_organoids/NoGradientBarplots.R')Dataset <- params$Dataset
logFCthr <- params$logFCthr
FDRthr <- params$FDRthr
FdrTh <- FDRthr
logFcTh <- logFCthr
SavingFolder <- ifelse(is.null(params$SavingFolder), getwd(), params$SavingFolder)
FiguresFolder <- ifelse(is.null(params$FiguresFolder), getwd(), params$FiguresFolder)
if (dir.exists(SavingFolder) == FALSE) {
dir.create(SavingFolder, recursive=TRUE)
}#SE object coming from DEA, but not containing specific contrast results
SE_DEA <- readRDS(params$SEFile)
SE_DEA <- SE_DEA[rowData(SE_DEA)$GeneName != '', ]
rownames(SE_DEA) <- rowData(SE_DEA)$GeneName
# List with differential expression results (all time-points)
DEA <- readRDS(params$DEAList)colvector <- c("#5ec962", "#e95462", "#2c728e")
names(colvector) <- c('All', 'Up', 'Down')if(! identical(rownames(SE_DEA), row.names(DEA$AR$res))){
stop('Expression data in SE and results from differential espression analysis are inconsistent.')
}
SE_DEA <- mergeDeaSE(SE_DEA, DEA$AR$res, subsetRowDataCols=NULL,
logFcCol='log2FoldChange', FdrCol='padj') #specify
## Renaming " log2FoldChange " to "logFC"
## Renaming " padj " to "FDR"16616 genes in 6 samples have been testes for differential expression.
The following number of genes are identified as differentially expressed:
Imposing a threshold of 0.55 on the Log2FC and 0.05 on the FDR (as specified in parameters), 25 genes are selected: 3 up-regulated genes and 23 down-regulated genes.
The results of the differential expression analysis are visualized by Volcano plot. An interactive version is included in the html (only genes with FDR < threshold), while a static version is saved.
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = FALSE)## Warning: Removed 16591 rows containing missing values or values outside the scale range
## (`geom_text_repel()`).
plotVolcanoSE(SE=SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr,
FdrCeil=1e-10, logFcCeil=4, Interactive = TRUE)Heatmaps for DEGs, showing scaled vst values.
DEGs <- dplyr::filter(data.frame(rowData(SE_DEA)), FDR < FDRthr & abs(logFC) > log2(logFCthr))
ScaledCols <- c('darkblue', "purple","white","lightgoldenrod1", 'goldenrod1')# sechm::sechm(SE_DEA, genes=DEGs$GeneName, assayName="vst", gaps_at="Genotype", show_rownames=FALSE,
# top_annotation=c('Genotype'), hmcols=ScaledCols, show_colnames=TRUE,
# do.scale=TRUE, breaks=0.85, column_title = "Scaled Vst Values")Gene ontology enrichment analysis is performed on the set of 25 genes using TopGO with Fisher statistics and weight01 algorithm.
For each specified domain of the ontology:
I generate vectors for the gene universe, all modulated genes, up-regulated genes and down-regulated genes in the format required by TopGo.
GeneVectors <- topGOGeneVectors(SE_DEA, FdrTh=FDRthr, logFcTh=logFCthr)## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
## Gene vector contains levels: 0,1
Therefore:
Then I set parameters according to the gene ontology domains to be evaluated. By default, Biological Process and Molecular Function domains are interrogated.
BpEval <- ifelse(length(grep('BP', params$TopGO))!=0, TRUE, FALSE)
MfEval <- ifelse(length(grep('MF', params$TopGO))!=0, TRUE, FALSE)
CcEval <- ifelse(length(grep('CC', params$TopGO))!=0, TRUE, FALSE)On the basis of the analysis settings, the enrichment for Biological Process IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
BPannAR <- topGO::annFUN.org(whichOnto="BP", feasibleGenes=names(GeneVectors$DEGenes),
mapping="org.Hs.eg.db", ID="symbol") %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResBPAllAR <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=BPannAR, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPAllAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11568 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15221 GO terms and 34588 relations. )
##
## Annotating nodes ...............
## ( 13536 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 611 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 4 nodes to be scored (0 eliminated genes)
##
## Level 13: 7 nodes to be scored (21 eliminated genes)
##
## Level 12: 14 nodes to be scored (58 eliminated genes)
##
## Level 11: 18 nodes to be scored (1172 eliminated genes)
##
## Level 10: 33 nodes to be scored (3034 eliminated genes)
##
## Level 9: 54 nodes to be scored (3865 eliminated genes)
##
## Level 8: 70 nodes to be scored (5031 eliminated genes)
##
## Level 7: 85 nodes to be scored (6310 eliminated genes)
##
## Level 6: 114 nodes to be scored (8325 eliminated genes)
##
## Level 5: 99 nodes to be scored (10031 eliminated genes)
##
## Level 4: 60 nodes to be scored (11961 eliminated genes)
##
## Level 3: 36 nodes to be scored (12905 eliminated genes)
##
## Level 2: 14 nodes to be scored (13195 eliminated genes)
##
## Level 1: 1 nodes to be scored (13273 eliminated genes)# Wrapper function for topGO analysis (see helper file)
ResBPDownAR <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=BPannAR, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPDownAR', outDir=paste0(SavingFolder))
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11568 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15221 GO terms and 34588 relations. )
##
## Annotating nodes ...............
## ( 13536 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 571 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 15: 2 nodes to be scored (0 eliminated genes)
##
## Level 14: 4 nodes to be scored (0 eliminated genes)
##
## Level 13: 7 nodes to be scored (21 eliminated genes)
##
## Level 12: 13 nodes to be scored (58 eliminated genes)
##
## Level 11: 17 nodes to be scored (1172 eliminated genes)
##
## Level 10: 30 nodes to be scored (2968 eliminated genes)
##
## Level 9: 48 nodes to be scored (3769 eliminated genes)
##
## Level 8: 63 nodes to be scored (4524 eliminated genes)
##
## Level 7: 80 nodes to be scored (5810 eliminated genes)
##
## Level 6: 108 nodes to be scored (7841 eliminated genes)
##
## Level 5: 93 nodes to be scored (9758 eliminated genes)
##
## Level 4: 57 nodes to be scored (11759 eliminated genes)
##
## Level 3: 34 nodes to be scored (12605 eliminated genes)
##
## Level 2: 14 nodes to be scored (13184 eliminated genes)
##
## Level 1: 1 nodes to be scored (13269 eliminated genes)
# Selection on enrichment of at least 2 is implemented (also to avoid depleted categories). Then categories are ranked by PVal and all the ones with Pval < th are selected. If the number is < minTerms, othter terms are included to reach the minimum number. GOTable(ResBPDownAR$ResSel, maxGO=20)ResBPUpAR <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=BPannAR, ontology='BP',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='BPUpAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 11568 GO terms found. )
##
## Build GO DAG topology ..........
## ( 15221 GO terms and 34588 relations. )
##
## Annotating nodes ...............
## ( 13536 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 94 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 12: 1 nodes to be scored (0 eliminated genes)
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 3 nodes to be scored (110 eliminated genes)
##
## Level 9: 6 nodes to be scored (197 eliminated genes)
##
## Level 8: 10 nodes to be scored (814 eliminated genes)
##
## Level 7: 10 nodes to be scored (1580 eliminated genes)
##
## Level 6: 15 nodes to be scored (2765 eliminated genes)
##
## Level 5: 19 nodes to be scored (5325 eliminated genes)
##
## Level 4: 12 nodes to be scored (7549 eliminated genes)
##
## Level 3: 10 nodes to be scored (10810 eliminated genes)
##
## Level 2: 6 nodes to be scored (11901 eliminated genes)
##
## Level 1: 1 nodes to be scored (12410 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/BPUp'), recursive=TRUE)
#GOAnnotation(ResBPUp$ResSel, GOdata=ResBPUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/BPUp'), keytype='SYMBOL')GOTable(ResBPUpAR$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResBPAllAR$ResSel, TopGOResDown=ResBPDownAR$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResBPAllAR$ResSel, TopGOResDown = ResBPDownAR$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Molecular Function IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
MFannAR <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResMFAllAR <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=MFannAR, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFAllAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4142 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4603 GO terms and 5944 relations. )
##
## Annotating nodes ...............
## ( 13956 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 105 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 9 nodes to be scored (941 eliminated genes)
##
## Level 7: 14 nodes to be scored (1149 eliminated genes)
##
## Level 6: 15 nodes to be scored (1309 eliminated genes)
##
## Level 5: 18 nodes to be scored (1608 eliminated genes)
##
## Level 4: 25 nodes to be scored (3294 eliminated genes)
##
## Level 3: 13 nodes to be scored (7327 eliminated genes)
##
## Level 2: 5 nodes to be scored (9225 eliminated genes)
##
## Level 1: 1 nodes to be scored (13454 eliminated genes)ResMFDownAR <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=MFannAR, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFDownAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4142 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4603 GO terms and 5944 relations. )
##
## Annotating nodes ...............
## ( 13956 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 103 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 10: 2 nodes to be scored (0 eliminated genes)
##
## Level 9: 3 nodes to be scored (0 eliminated genes)
##
## Level 8: 9 nodes to be scored (941 eliminated genes)
##
## Level 7: 14 nodes to be scored (1149 eliminated genes)
##
## Level 6: 15 nodes to be scored (1309 eliminated genes)
##
## Level 5: 18 nodes to be scored (1608 eliminated genes)
##
## Level 4: 24 nodes to be scored (3294 eliminated genes)
##
## Level 3: 12 nodes to be scored (7327 eliminated genes)
##
## Level 2: 5 nodes to be scored (9171 eliminated genes)
##
## Level 1: 1 nodes to be scored (13444 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFDown'), recursive=TRUE)
#GOAnnotation(ResMFDown$ResSel, GOdata=ResMFDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFDown'), keytype='SYMBOL')GOTable(ResMFDownAR$ResSel, maxGO=20)ResMFUpAR <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=MFannAR, ontology='MF',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='MFUpAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 4142 GO terms found. )
##
## Build GO DAG topology ..........
## ( 4603 GO terms and 5944 relations. )
##
## Annotating nodes ...............
## ( 13956 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 5 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 4: 1 nodes to be scored (0 eliminated genes)
##
## Level 3: 2 nodes to be scored (0 eliminated genes)
##
## Level 2: 1 nodes to be scored (224 eliminated genes)
##
## Level 1: 1 nodes to be scored (11430 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/MFUp'), recursive=TRUE)
#GOAnnotation(ResMFUp$ResSel, GOdata=ResMFUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/MFUp'), keytype='SYMBOL')GOTable(ResMFUpAR$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResMFAllAR$ResSel, TopGOResDown=ResMFDownAR$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResMFAllAR$ResSel, TopGOResDown = ResMFDownAR$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`On the basis of the analysis settings, the enrichment for Cellular Component IS performed.
# I generate a list that contains the association between each gene and the GO terms that are associated to it
CCannAR <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(GeneVectors$DEGenes),
mapping='org.Hs.eg.db', ID='symbol') %>% inverseList()
# Wrapper function for topGO analysis (see helper file)
ResCCAllAR <- topGOResults(Genes=GeneVectors$DEGenes, gene2GO=CCannAR, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCAllAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1699 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1921 GO terms and 3231 relations. )
##
## Annotating nodes ...............
## ( 14191 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 91 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 8 nodes to be scored (0 eliminated genes)
##
## Level 9: 9 nodes to be scored (72 eliminated genes)
##
## Level 8: 13 nodes to be scored (499 eliminated genes)
##
## Level 7: 11 nodes to be scored (1457 eliminated genes)
##
## Level 6: 14 nodes to be scored (5828 eliminated genes)
##
## Level 5: 10 nodes to be scored (7323 eliminated genes)
##
## Level 4: 10 nodes to be scored (10438 eliminated genes)
##
## Level 3: 12 nodes to be scored (13355 eliminated genes)
##
## Level 2: 2 nodes to be scored (13807 eliminated genes)
##
## Level 1: 1 nodes to be scored (14092 eliminated genes)
#write.table(ResCCAll$ResAll, file=paste0(SavingFolder, 'TopGO/CCAllResults.txt'), sep='\t', row.names=FALSE)# Wrapper function for topGO analysis (see helper file)
ResCCDownAR <- topGOResults(Genes=GeneVectors$DEGenesDown, gene2GO=CCannAR, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCDownAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1699 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1921 GO terms and 3231 relations. )
##
## Annotating nodes ...............
## ( 14191 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 91 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 11: 1 nodes to be scored (0 eliminated genes)
##
## Level 10: 8 nodes to be scored (0 eliminated genes)
##
## Level 9: 9 nodes to be scored (72 eliminated genes)
##
## Level 8: 13 nodes to be scored (499 eliminated genes)
##
## Level 7: 11 nodes to be scored (1457 eliminated genes)
##
## Level 6: 14 nodes to be scored (5828 eliminated genes)
##
## Level 5: 10 nodes to be scored (7323 eliminated genes)
##
## Level 4: 10 nodes to be scored (10438 eliminated genes)
##
## Level 3: 12 nodes to be scored (13355 eliminated genes)
##
## Level 2: 2 nodes to be scored (13807 eliminated genes)
##
## Level 1: 1 nodes to be scored (14092 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCDown'), recursive=TRUE)
#GOAnnotation(ResCCDown$ResSel, GOdata=ResCCDown$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCDown'), keytype='SYMBOL')GOTable(ResCCDownAR$ResSel, maxGO=20)# Wrapper function for topGO analysis (see helper file)
ResCCUpAR <- topGOResults(Genes=GeneVectors$DEGenesUp, gene2GO=CCannAR, ontology='CC',
desc=NULL, nodeSize=5, algorithm='weight01', statistic='fisher',
EnTh=params$GoEnTh, PvalTh=params$GoPvalTh, minTerms=15, geneTh=4,
saveRes=TRUE, fileName='CCUpAR', outDir=SavingFolder)
## Gene vector contains levels: 0,1
##
## Building most specific GOs .....
## ( 1699 GO terms found. )
##
## Build GO DAG topology ..........
## ( 1921 GO terms and 3231 relations. )
##
## Annotating nodes ...............
## ( 14191 genes annotated to the GO terms. )
##
## -- Weight01 Algorithm --
##
## the algorithm is scoring 12 nontrivial nodes
## parameters:
## test statistic: fisher
##
## Level 7: 1 nodes to be scored (0 eliminated genes)
##
## Level 6: 2 nodes to be scored (0 eliminated genes)
##
## Level 5: 2 nodes to be scored (897 eliminated genes)
##
## Level 4: 3 nodes to be scored (6555 eliminated genes)
##
## Level 3: 2 nodes to be scored (10965 eliminated genes)
##
## Level 2: 1 nodes to be scored (11443 eliminated genes)
##
## Level 1: 1 nodes to be scored (12601 eliminated genes)
#dir.create(paste0(SavingFolder, 'TopGO/CCUp'), recursive=TRUE)
#GOAnnotation(ResCCUp$ResSel, GOdata=ResCCUp$GOdata, SavingFolder=paste0(SavingFolder, 'TopGO/CCUp'), keytype='SYMBOL')GOTable(ResCCUpAR$ResSel, maxGO=20)topGOBarplotAll(TopGOResAll=ResCCAllAR$ResSel, TopGOResDown=ResCCDownAR$ResSel,
terms=12, pvalTh=0.05, plotTitle=NULL, gradient = FALSE, cols = colvector)
## Warning in topGOBarplotAll(TopGOResAll = ResCCAllAR$ResSel, TopGOResDown = ResCCDownAR$ResSel, : Please make sure you specified names of the cols vector correctly.
## See `?topGOBarplotAll`Most of the useful information has been saved during the analysis. Here I save figures, workspace and information about the session.
if (params$SaveImages == TRUE){ #Just in case since eval only works when knitting
#Set the folder paths
from <- paste(getwd(), paste(params$NbName, 'files/figure-html', sep='_'), sep='/')
to <- params$FiguresFolder
#Copy to output directory
file.copy(from, to, recursive = TRUE, copy.mode = TRUE)
}SessionInfo <- sessionInfo()
Date <- date()
save.image(paste0(SavingFolder, '/ipsc.', 'FunctionalAnalysisWorkspace_AR.RData'))